The present invention relates to commercial electric power generation.
Wind energy is an important energy source for utility companies (“utilities”) throughout the world. A wind farm of wind turbines operated for commercial electric power generation harvests wind energy and converts the harvested wind energy into electric power. The generated electric power, i.e., the wind farm's energy output, is then distributed to utility companies. As the electric power generated by the wind farm becomes a greater share of a utility company's resource stack, it is necessary to predict the energy output of the wind farm so that a wind farm operator can meet the varying demand for electric power. Utilities receiving electric power from the wind farm are thus requiring forecasts of energy output from the wind farm operator. The time period of interest for forecasts varies from hourly forecasts for dispatching and scheduling to one-day or two-day forecasts for spot market purchases and sales.
Generally, a conventional forecast system uses regional weather forecasts and the capacity of the wind turbines of the wind farm to forecast the energy output of the wind farm. Regional weather forecasts provided to wind farm operators are based on model output statistics. Typically, the model is a set of multiple regression equations that need to be updated as independent variables and dependent variables change. Examples of changes in variables include improvements to regional forecasts models or changes in the wind farm such as changes in the number of turbines, turbine availability, and turbine performance.